# Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle

arXiv: https://arxiv.org/abs/2507.05723 · HTML: https://mlsanity.com/paper/2507.05723/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 17 of 17)

- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities](https://mlsanity.com/paper/2310.10436/) (2023)
- [Simulating Opinion Dynamics with Networks of LLM-based Agents](https://mlsanity.com/paper/2311.09618/) (2023)
- [Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://mlsanity.com/paper/2402.16333/) (2024)
- [Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents](https://mlsanity.com/paper/2402.12327/) (2024)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [Do Large Language Models Solve the Problems of Agent-Based Modeling? A Critical Review of Generative Social Simulations](https://mlsanity.com/paper/2504.03274/) (2025)
- [LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities](https://mlsanity.com/paper/2405.06700/) (2024)
- [Agent-Based Modelling Meets Generative AI in Social Network Simulations](https://mlsanity.com/paper/2411.16031/) (2024)
- [Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat](https://mlsanity.com/paper/2401.17163/) (2024)
- [GPT-Based Models Meet Simulation: How to Efficiently Use Large-Scale Pre-Trained Language Models Across Simulation Tasks](https://mlsanity.com/paper/2306.13679/) (2023)
- [A brief history of AI: how to prevent another winter (a critical review)](https://mlsanity.com/paper/2109.01517/) (2021)
- [TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions](https://mlsanity.com/paper/2410.21280/) (2024)
- [Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning](https://mlsanity.com/paper/2402.02388/) (2024)
- [Exploring the Potential of Conversational AI Support for Agent-Based Social Simulation Model Design](https://mlsanity.com/paper/2405.08032/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challenges](https://mlsanity.com/paper/2602.05883/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception](https://mlsanity.com/paper/2603.20750/) (2026)
- [Mechanism Plausibility in Generative Agent-Based Modeling](https://mlsanity.com/paper/2605.12824/) (2026)

## Abstract (arXiv)

The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.